As a natural extension of the Compensation Principles outlined above, and our commitment to transparency, sharing, efficiency, directness, and boring solutions (amongst other values), we developed a Compensation Calculator that we are rolling out for those roles in which we have the most contributors, and thus for whom the question about "what is fair compensation" comes up most frequently.
As with all things at GitLab, the compensation calculator is a constant work in progress. Please send an email to Ernst if/when you find a big difference between what the calculator suggests vs. what market data indicates. Please make sure to include all relevant links and data.
Your compensation =
Benchmark compensation x
Rent Index + 25% x
Level Factor x
Experience Factor x
Contract Type Factor
Benchmark compensationis typically a median employee salary for the role in New York, as found on publicly available sources such as Payscale and others.
Rent Indexis taken from Numbeo, which expresses the ratio of cost of rent in many metro areas to the average cost of rent in New York City (i.e. Rent Index in New York = 1.00). As explained in more detail below, we add 25% to this regardless of location, which reflects the fact that we hire better than median performers. If your metro area is not listed, please list the nearest one that seems comparable.
Level Factoris currently defined as junior (0.8), intermediate (1.0), senior (1.2), or lead (1.4)
Experience Factorfalls between 80% - 120%
Contract Type Factordistinguishes between employee or contractor, and can be a different factor in each country; see below for further explanation.
In developing the compensation formula above, we looked at the compensation of our team members which had been set in the past (without the formula), and found out that there was a statistically significant correlation between compensation and the factors that are now in the formula. We purposefully chose to look for correlations with metrics that are probably causal and definitely relevant in people's lives (the rent!). This also has the advantage of letting us work with data that is readily available publicly, as opposed to trying to scour the web for market compensation rates for all roles in all locations. Perhaps surprisingly, there was a stronger correlation between compensation and rent index than with the more general cost of living index available through Numbeo (or the cost of living with rent index, for that matter); and so we moved ahead with the Rent Index.
It was a small step to go from the initial linear regression to picking the coefficients that are now in the formula, except that we 'discovered' that an offset was needed in the Rent Index to make things work (i.e. to have the formula 'predict' compensations that were in line with current actual compensations). As a consequence of this offset of 25%, for an employee in New York their median compensation will be 25% higher than what the New York benchmark would have suggested. This probably reflects that we generally hire better than 'median' performers.
The contract type factor helps to make the distinction between an employee and a contractor for those countries where we offer both contract types. For example, a typical contractor may have to bear the costs of their own health insurance, social security taxes and so forth, leading to a higher compensation for the contractor. As another example the costs also vary for employees between countries, for example if there are more government mandated programs, this commonly leads to more costs for the employer and a lower pay for the individual. The contract type factor is meant to capture these differences.
This list will be expanded as we gain more experience with the calculator and as we are able to offer employee contracts (as opposed to only contractor contracts) in various countries. Visit our contracts page to learn more about the different types of contracts we offer.